A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

  • Kashif Hussain University of Electronic Science and Technology of China
  • Noreen Talpur Universiti Teknologi PETRONAS
  • Muhammad Umar Aftab University of Electronic Science and Technology of China
  • Zakria University of Electronic Science and Technology of China
Keywords: Fuzzy systems, Student performance prediction, Henry gas solubility optimization, ANFIS

Abstract

Data mining is being increasingly leveraged in educational settings for achieving various different outcomes including students' learning patterns, course and teaching outcome assessment, and students' expected achievement prediction. Utilizing data collected from daily curricular and non-curricular activities, machine learning techniques have benefited administrators in making efficient decisions. Based on students' behavioral information, this research proposes student performance prediction model using fuzzy-based neural network (FNN) trained by a novel metaheuristic approach. Because original gradient-based learning method associated with FNN limits its performance, this research employs Henry Gas Solubility Optimization (HGSO) algorithm for tuning FNN parameters. The empirical analysis suggests superiority of results produced by the proposed approach as compared with the FNN trained by the competitive methods.

Published
04-03-2020
How to Cite
Hussain, K., Talpur, N., Aftab, M. U., & Zakria. (2020). A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students’ Performance Prediction. Journal of Soft Computing and Data Mining, 1(1), 1-9. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/jscdm/article/view/6825
Section
Articles